# # S 13300; L 13785
# CUDA_VISIBLE_DEVICES=6 python few-shot.py \
# --n_train 3 \
# --epochs 50 \
# --batch_size 5 \
# --resolution 256 \
# --save_dir save/00006-images-mirror-low_shot-kimg10000-batch32-color-translation-cutout \
# --split 7 1 2 \
# --which_net L \
# --which_repre_layers 4,8,16,32,64,128,256 \
# --shot 10

CUDA_VISIBLE_DEVICES=2 python auto-shot-ds.py \
--n_train 3 \
--mode U-Net \
--save_dir save/00011-GAN_OASIS-mirror-low_shot-kimg25000-batch32-color-translation-cutout \
--out_dir ./save_seg/ds \
--train_dir data/OASIS-128-160-norm \
--test_dir data/CANDI-128-160-norm \
--shots 128 \
--threshold 7 \
--epochs 3000 \
--batch_size 16 \
--split 72 8 20 \
--which_net BiFPN \
--which_repre_layers 4,8,16,32,64,128,256 \
--w_steps 1000 \
--length 128 \
--multi_class \
--combine \
--resize \
--myds \
--seed 0

# CUDA_VISIBLE_DEVICES=2 python supervised.py \
# --n_train 3 \
# --data_dir ./data/OASIS-128-160-norm \
# --out_dir ./save_seg/supervised \
# --epochs 40 \
# --batch_size 16 \
# --resolution 160 \
# --split 72 8 20 \
# --n_class 16 \
# --multi_class \
# --combine \
# --shot 257 \
# --aug \
# --length 128 \
# --w_steps 1000